Dynamic Quantum Topology Adaptive Quantum Algorithm Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper investigates the application of dynamic quantum topology to optimize quantum algorithms, focusing on quantum machine learning and quantum optimization. The core concept is to dynamically adjust the quantum topology structure of a quantum system, enabling the algorithm to explore different solutions more effectively. We propose a novel algorithm that iteratively refines the quantum state through a series of topological transformations, leveraging the inherent properties of quantum topology to guide the optimization process. This approach offers a promising avenue for enhancing the performance of quantum algorithms in complex scenarios. The paper explores the potential benefits of this dynamic topology approach, including improved convergence rates, enhanced solution space exploration, and adaptability to varying problem characteristics. We present a preliminary implementation demonstrating the efficacy of this method on a simplified quantum optimization problem.

DOI: 10.5281/zenodo.22290181

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